System and method for database privacy protection

Abstract

The invention relates to a system and a method for privacy preservation of sensitive attributes stored in a database. The invention reduces the complexity and enhances privacy preservation of the database by determining the distribution of sensitive data based on Kurtosis measurement. The invention further determines and compares the optimal value of k-sensitive attributes in k-anonymity data sanitization model with the optimal value of l sensitive attributes in l diversity data sanitization model using adversary information gain. The invention reduces the complexity of the method for preserving privacy by applying k anonymity only, when the distribution of the sensitive data is leptokurtic and optimal value of k is greater than the optimal value of l.

Description

FIELD OF THE INVENTION[0001]The present invention relates to a method and a system for privacy preservation of a sensitive data. More particularly, the invention relates to the method and the system for reducing complexity and enhancing privacy preservation of the sensitive data in one or more database.BACKGROUND OF THE INVENTION[0002]Storing data for the use in future for various purposes is a well known and frequently followed technique now days. Privacy preserving data mining is a research area concerned with protecting privacy derived from personally identifiable information in the process of data mining. Today privacy is an essential feature for both a user as well as an enterprise. Many organizations collect data for scientific research as well as for market analysis. Access to this data is also given to third parties for further productive analysis. The available datasets pose serious threats against the privacy of individuals and organizations. In view of this concern to add...

AI Technical Summary

Benefits of technology

The present invention provides a method for reducing the complexity of a data sanitization model while maintaining the same level of privacy. This is achieved by measuring the distribution pattern of sensitive attributes in a database and comparing it with the adversary information gain for a k-anonymity data sanitization model and a k-anonymity l-diversity data sanitization model. The method allows for optimal values to be computed for l and k, and only uses the k-anonymity model when k is greater than l and the distribution pattern is leptokurtic, providing the same level of privacy protection as the k-anonymity l-diversity model.

Problems solved by technology

The available datasets pose serious threats against the privacy of individuals and organizations.

All these methods are frequently used but they involve utility loss.

However, suppression can drastically reduce the quality of the data and in the case of statistical use; overall statistics can be altered, rendering the data practically useless.

The associated problem with these solutions is that they are NP-hard in nature as they are basically search over space of possible multi-dimensional solutions.

Also the high dimensionality of these techniques adds computational overhead.

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